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Tuesday, 27 May 2014

Let's try to make Bengtssongate into a learning opportunity. First he joined the Global Warming Policy Foundation, a group you should not criticise for their opinion because they officially do not have an opinion, just bias. Then he left them again writing a completely inconsistent resignation letter. In the last act of this drama a British (!) newspaper reported that a manuscript of his was once rejected. Rejected manuscripts are not news, but a fact of academic life. For more background see Climate and Stuff. If our local tabloid has such headlines; it makes me happy, nothing bad has happened in the world.

But maybe the discussion on peer review and gatekeeping is interesting. On the blog Bishop Hill, Radical Rodent (I like alliteration) summarised the main misunderstandings I would like to clarify in this post:

Personally, I am a little perplexed as to why a paper has to be peer-reviewed before it is seriously considered for reading. What is wrong with presenting your paper on completion, and let the general public decide its merits? For most papers, the readership will probably be the few who have any interest or knowledge in the subject, thus will be able to make up their own minds as to whether or not the author is publishing poppycock or not. The present “has to be peer-reviewed” ethos gets the bizarre situation where one party in an argument will not look at another’s supporting documents as they have not been peer-reviewed; a very narrow-minded attitude, I am sure you will agree.

Scientific literature

The centre of science is the scientific literature. The high quality standards (nowadays enforced by by peer review) give the scientific literature this special role. Conferences are nice to get your work known, to debate new ideas and directions and to get to know colleagues for future collaborations, but the real scientific debate is in the literature. In the best case, blogs and social media could provide a role similar to conferences while including a even broader group of people.

Getting published in the scientific literature is not so hard. Especially in the natural sciences the template is quite clear. Just do something new and interesting. Make clear that you know the scientific literature in the introduction and thus that you are sure that your work is really new. Describe the work of others (potential reviewers) fairly. Write up clearly what you did (methods) and found (results). Avoid subjective and emotional terms. Compare your results to those of others in the discussions. Avoid mixing results and discussion as well as possible. Do not draw conclusions that go beyond your findings. If the reviewers want changes, implement the good ideas, clarify the parts of the manuscript that provoked the bad suggestions, politely request that you might address extensive requests in future studies.

There are also some weird aspects. It is better to write passive sentences. While it makes the text harder to read, it suggests objectivity. It is important that the text, references and figure look neat. The reviewer cannot see your desk and computer code to judge whether you worked carefully; the neatness of the manuscripts is a proxy for this. For the same reason figures prepared in MS Excell look less professional. The points mentioned in this paragraph should, of course, be unimportant, but scientists are also humans.

Peer review

There is much wrong with peer review. Richard Horton (editor with the top medical journal the lancet) wrote (h/t esmiff):

The mistake, of course, is to have thought that peer review was any more than a crude means of discovering the acceptability — not the validity — of a new finding. Editors and scientists alike insist on the pivotal importance of peer review. We portray peer review to the public as a quasi-sacred process that helps to make science our most objective truth teller. But we know that the system of peer review is biased, unjust, unaccountable, incomplete, easily fixed, often insulting, usually ignorant, occasionally foolish, and frequently wrong.

This is all true, except for the claims of holiness, even if a little one sided. The other side is that peer review helps authors write better articles (feedback). And it helps readers find better articles (selection). And what is the alternative?

Unfortunately you have to read a text before you know whether it was worthwhile and sometimes you have to do research or need specialised expertise to see the problems. Hence these low quality standards, or rather no quality standards, slow down scientific progress. That is the basic reason why peer review has become a standard in science.
It hasn't always been like this (h/t Bishop Hill). In the beginning people would simply write books without any peer review. Next to the reputation of the author, the amount of work that went into writing, copying or printing and distributing the books was the main quality filter at the time. When the first scientific journals appeared the editors often made the decision by themselves and were the ones that put their reputation on the line. Only after the WWII did peer review become the standard in scientific publishing.

Without peer review every author would have to build up credibility himself. However, one can only get to know a relatively small number of people sufficiently well to know whether their work is worthwhile. Thus maybe somewhat paradoxically, peer review actually helps less known, young and new scientists to be heard. This is more important nowadays as the number of scientists is somewhat larger than in Newton's time.

As I have argued in more length before, in the same way peer review also helps fringe ideas. If you read about a weird idea at WUWT you will naturally assume it is wrong. If you read about the same weird idea in the scientific literature you will be more willing to invest your time to consider its merits.

Peer review is also important for people who are not experts in a certain field. Previously I mentioned the press as an example, but scientists from other fields also benefit by knowing that an article is probably okay. In this way peer review helps interdisciplinary research. This may be another reason why peer review has become more important in the last decades.

Concluding, peer review is gatekeeping, but its practice actually makes science more open to new people, ideas and scientists from other fields. We should not be blind to its problems and we should strive to keep on improving standards. By this I do not mean stricter standards, but that standards should be more consistently applied. Altogether, the scientific literature is clearly better with peer review as it is without.

Had Bengtsson's intention been to maximally hurt climate science and delay action as much as possible, the formulation could not have been much better. Had his intention been to facilitate future collaborations, I would suggest some improvements, but is too late for that now.

Maybe it becomes clear if you shorten and simplify his resignation mail, printed in full further below:

I am resigning because my colleagues object to me being in the GWPF and I would like to be able to keep doing science. My colleagues are evil and suppress my freedom of opinion.

What I had written, had I wanted to continue collaborating with my colleagues:

I am resigning. After warnings from my colleagues, I have investigated the background of the GWPF in more detail and feel that it would be better not to be in its advisory board. I apologise to my colleagues for naively joining an anti-science organization.

Collaboration

Next to this inconsistency, I would be surprised if a significant number of his colleagues would no longer want to work with him, even now after bringing up McCarthy. This is not how the scientists I know behave. Personally I have no problem collaborating with a colleague with a different opinion, as long as the science is good. I would expect that most of my colleagues would handle this similarly.

For example, I really dislike the concept of the blog The Climate Onion of Hans von Storch and colleagues. Already its slogan about trying to be an "honest broker", therewith implying that other scientists are not honest. If you are not polite to climate "sceptics" in the comments there are threats of moderation or banning, whereas you can say the most evil things about mainstream science and scientists. No problem.

Seen from the other side, I am sure that Hans von Storch will find my blog horrible. Because I have no problem with calling out the bad (or maybe better formulated the fake) "science" of WUWT and Co. To me the hallmark of science is the strong claims should be backed by strong evidence. That has brought us scientific progress. To keep my mouth shut for political appeasement reasons when WUWT and Co. violate science, I would view as treason to science, the Enlightenment and our open societies.

But, how to best communicate science is not a scientific question and I guess we will have to agree to disagree on this matter. That is thus no reason not to collaborate. Von Storch has written many good and interesting papers that I naturally read. Only last year I have asked him to join a research proposal to the German science foundation and he accepted to strengthen the proposal with his reputation. While coalitions and conflicts naturally exist, like in any human enterprise, I would say that such collaborations are normal in science and that scientists make an effort to ignore non-scientific noise.

Most coalitions are simply about competition and personal dislikes, not about the right value of the climate sensitivity. And the coalitions are typically not very defined and many are on talking terms with multiple coalitions and provide a network that facilitates communication. The only conflict I know that escalated so much that people are no longer on speaking terms might in retrospect have been an incursion of the political climate "debate" into science.

This conflict was on the topic of Long Range Dependence (LRD). This work suggests that the confidence interval for trends is larger than expected from using traditional methods. Here I know of a professor that does not allow his employees to talk to mainstream scientists. A damning sign of insecurity, which may be warranted be given of the low quality standards in part of this community (Maraun et al., 2004; Rust et al., 2008). This is a pity because the problem is probably real and important. The conflict culminated in a conference where a mainstream scientist asked someone of the LRD community to read Karl Popper (about falsifiability). To which a LRD professor replied: "Well first of all you have to believe in LRD." I would argue that believing is for religion and he should convert the idea to falsifiable science and provide evidence no reasonable scientist can reject, but I digress.

Resignation mail

Bengtsson's resignation mail was published with permission on the Climate Onion.

I have been put under such an enormous group pressure in recent days from all over the world that has become virtually unbearable to me. If this is going to continue I will be unable to conduct my normal work and will even start to worry about my health and safety.

I see therefore no other way out therefore than resigning from GWPF. I had not expecting such an enormous world-wide pressure put at me from a community that I have been close to all my active life. Colleagues are withdrawing their support, other colleagues are withdrawing from joint authorship etc. I see no limit and end to what will happen. It is a situation that reminds me about the time of McCarthy.

I would never have expecting anything similar in such an original peaceful community as meteorology. Apparently it has been transformed in recent years.

Under these situation I will be unable to contribute positively to the work of GWPF and consequently therefore I believe it is the best for me to reverse my decision to join its Board at the earliest possible time.

If I would make a big mistake like joining the GWPF, I would hope that some colleagues of mine would be honest enough and ask me what the hell I am doing and whether I know what kind of organisation that is. My main fear would be that because of the scientific culture of not getting personal no one would have the guts to point out my mistake.

Especially Bengtsson having occupied high positions, director of the ECMWF and the Max Planck Institute, may not be used to people giving honest feedback for several decades and thus found this stressful. Many seem to have interpreted "health and safety" as a physical threats to Bengtsson. I guess that the stress from the feedback may be sufficient to make a man in progressed age worry about his health.

The story about Bengtsson in The Times suggests that it was also just one colleague that did not want to work with him any more. Not the multiple ones his resignation suggests. Especially if this colleague was a direct victim of an anti-science campaign by the GWPF, I can understand this. I would hope that people still have the freedom to chose with whom to collaborate. One of the climate "sceptics", who are typically libertarians, suggested that we should use the coercive power of the state to force scientists to collaborate with Bengtsson. Almost funny.

If scientists voice concerns that a colleague is joining forces with a science denier organisation it's McCarthy-ism (in denier land). If a US Senator says he has a list of scientists that he wants criminally prosecuted it's not McCarthy-ism. (It's Inhofism.)

If you have a strong stomach and want to know how anti-science spinns the story, a good start is State of the Climate.

Joining the GWPF was not the first political act of Bengtsson. Before he had one of the most read posts at the Climate Onion on political solutions for climate change. Note that in the comments no "sceptic" complains about his advocacy as they would do for mainstream scientists. And note that Bengtsson complains about the politicisation of climate science, but seems not to relate that to his own actions, like a good climate "sceptic".

Thursday, 8 May 2014

This is part 3 of a series on what we know about changes in weather variability on climatic time scales. This series is more technical as most posts on this blog and more directed at the scientific community. The introduction gives some background on the topic. If you did not read this post back in November, you might want to do that now before continuing. Variability is not as trivial as some may think.

The logical next step would have been to have a look at how weather variability is changing in reality. However, this will take more than one post, thus I will start in this post with what we know from dynamical climate models and how good the models are able to reproduce weather variability.

Temperature

A number of recent model simulation studies address changes in the temperature variability in the past and future. Fitting to what we will next see in the next post on observations, the year to year variability typically decreases, while increases are found at daily time-scales.

Huntingford et al. (2013) computed the year to year (interannual) temperature variability by subtracting a running mean from the simulated global annual mean temperature. Their analysis of this variability in 17 CMIP5 simulations (the main intercomparison project for global climate models) does not show large variability decreases up to the present time; but their study does predict (RCP8.5 scenario) that globally the variability is expected to decrease in the coming century (Figure 1).

Figure 1. The global standard deviation of the annual temperature derived for two window sizes to compute the anomalies: 11 years (top) and 31 years (bottom). The black line shows the average, the blue area the spread of the ensemble of 17 climate models with historical-plus-RCP8.5-scenario simulations in the CMIP5 database. From Huntingford et al. (2013).

A more detailed study on the temperature variability of a 10-member multi-model RCM ensemble (a large number of runs from regional climate models) produced by the PRUDENCE project for Europe shows increases for the summer temperature variability at various temporal scales from daily to inter-seasonal (the variability in summer means from year to year). The increases are seen in the average variability over all of Europe, but are especially strong in what they call the Transitional Climate Zone (TCZ), the region between the Mediterranean and the Nordic countries (Fischer and Schär, 2009). In this zone, soil moisture is very important in the summer. More to the South, in the Mediterranean countries, the soil is typically dry in summer. More to the North soil moisture is also plentiful in summer.

Figure 2. Simulated change in total daily temperature variability (K) in the period 2071–2100 with respect to the current climate (control run). Figure 2b of Fischer and Schär (2009)

Also Beniston (2004) finds an increase in variability: the daily maximum temperatures averaged over the summer in the PRUDENCE dataset increases by "only" 5°C in the South-West of France at the end of this century, whereas the upper extremes increase more: about 6 to 8°C.

The striking differences between the above mentioned results, decreasing variability (Huntingford et al.) and increasing variability (Fischer & Schär and Beniston) is most likely due to the difference in the temporal extend (annual vs. summer), but could also be due to the area considered (global vs. Europe). The relatively small methodological differences are probably not the reason.

The importance of methodology, however, is seen by comparing Fischer and Schär (2009) with the study on the same PRUDENCE dataset by Ballester et al. (2010). They analyse daily temperature data, but instead of one season (summer) they take the full year. On this dataset they study the importance of the first three moments (mean, standard deviation and skewness). Since they keep the annual cycle, the calculated standard deviation is dominated by the annual cycle, whereas in Fischer and Schär the variability was to a large part day to day variability. This may explain the findings of Ballester et al., which indicate that the changes in the warm percentiles are mainly determined by the mean temperature, not by the standard deviation or skewness. Ballester et al. (2010) do find the variance and skewness to be important to explain changes in the cold tail of the temperature distribution.

Precipitation

An increase in short- and long-duration extreme precipitation is projected in the PRUDENCE ensemble for most of Europe, even for regions in Central Europe where mean precipitation is projected to decrease (Christensen and Christensen, 2003; Fowler et al., 2007). This indicates that the variability of precipitation will increase as expected by Trenberth (1999).

In a recent study Maraun (2013) studied trends in the season maximum of daily precipitation in ENSEMBLE projections using a Generalised Extreme Value (GEV) distribution. He found that both the location (related to the mean value of the extremes) and the scale parameter (related to the width of the distribution of the extremes) of the GEV needed to have a trend component. Otherwise trend in the seasonal maxima would be underestimated. This indicates that changes in variability are important for extreme precipitation.

Pressure

Also the variability of air pressure may change as suggested by many studies on changes in (winter) storms. Zahn and Von Storch (2010) showed that the frequency of North Atlantic polar lows is projected to decline in response to future climate warming. Chen and Von Storch (2013) studied the climatology of North Pacific Polar Lows in downscaled reanalysis data. This climatology is consistent with the limited observational evidence and exhibits strong year-to-year variability, but weak decadal variability and a small positive trend.

Model variability validation

The scientific literature shows substation deviations between modelled and observed variability for many variables and temporal scales. Some examples are summarized below; a much too limited number to draw firm conclusions. If these deviations are due to model deficiencies (and not the observations) this is not only problematic for studies on extreme weather, but also for accurate climate model simulations of the mean model state. Climate models contain many nonlinear processes (such as radiative transfer and precipitation production) and threshold-like processes (such as phase transitions and wilting of plants). To model such nonlinear processes the variability is paramount.

Temperature variability of the European PRUDENCE ensemble was validated with ECA&D station data and a gridded ENSEMBLES observational dataset by Fischer and Schär (2009). They found that the models typically strongly overestimate the total daily summer variability and the interannual variability, by up to a factor 2. The models that overestimate the current intraseasonal variability tend to be the ones that showed larger increases in the period 2071-2100.

Lovejoy et al. (2013) validate the temperature fluctuations of GCM runs against observations, reanalysis data and multi-proxy reconstructions. They do not study the absolute amount of variability, but how the variability changes as a function of temporal scale across several orders of magnitude. Whereas at small scales the relationships are relatively good, at larger scales they can even have the wrong sign. They attribute this to missing long-term processes in the models, but errors in the empirical datasets can also not be excluded and the paper compares unforced simulations with empirical datasets that include anthropogenic global warming.

Precipitation

Schindler et al. (2012) validate the seasonal cycle of extreme precipitation in the UK in the ENSEMBLE dataset. The strong seasonal cycle in North-West Scotland is well reproduced, but only few models are able to reproduce the strong seasonal cycles in East-Anglia. Furthermore, they found that in general spring and fall have the lowest biases, whereas extreme precipitation in winter is too strong and in summer is too low.

For the Alpine region (Frei et al., 2006) found that RCM model biases for extreme precipitation are comparable to or even smaller than those for wet day intensity and mean precipitation. The model differences are well explained by differences in the precipitation frequency and intensity process (Frei et al., 2006).

Volosciuk et al. (submitted, 2013) study the influence of the horizontal and vertical model resolution for ECHAM5 on extreme value statistics. They compare the differences at a common coarse grid and find that return level generally decrease with coarser model resolution. Also regional patterns change, for instance a coarse vertical resolution results in a shift in extreme precipitation toward the equator. In contrast to these results for extreme precipitation, the impact on average precipitation of vertical resolution is less pronounced, whereas the impact of the horizontal resolution is negligible. This suggests that the mentioned result for the extremes are due to the precipitation variability.

Concluding

This is unfortunately just a blog post and not a review article (if anyone want to help write one, please say so). I have probably still not read a considerable part of the literature. Still the results suggest that the models show similar changes on climatic scales as the measurements: Less variability in temperature from year to year, more variability on daily scales. And on daily scales the variability of precipitation is increasing and will further increase.

The authors of validation papers tend to focus on what does not work well yet. Still if the sample above is representative, the modelling of variability still shows considerable deficiencies. Surprisingly temperature seems to be as difficult as precipitation, in as far as one can compare two dislike variables, whereas for the means precipitation is seen as a difficult variable.

My impression about the model deficiencies is at least shared by one reviewer. A research proposal of mine on weather variability was just rejected with as reason that the models are not able to model variability sufficiently well. I would argue that should have been the reason for acceptance. If we cannot do weather variability, we also cannot do extreme weather. And many people are working on that. Shouldn't they know?

Raw climate records contain changes due to non-climatic factors, such as relocations of stations or changes in instrumentation. This post introduces an article that tested how well such non-climatic factors can be removed.

Tuesday, 6 May 2014

The point is that you can’t neutralize plausible alternative interpretations of the available evidence from diminishing the scientific ‘consensus.’ It only takes one such argument, and one person making it (but in fact there are numerous arguments and a substantial number of people making them).

In practice it will likely take more than one Galileo or study. Also the refutation of classical mechanics by quantum mechanics and relativity did not change many things we already understood at the time. It allowed us to study new things and ask new questions. That was the revolution.

Climatology is a mature field and new findings will more likely change the complete picture only little. The largest uncertainties are in the impacts, improving our understanding there will have to be done one impact at a time. And more likely, one aspect of an impact at a time.

It is a pity that people did not respond to my claim that large scientific changes (paradigm changes) tend to enlarge the scope of science, rather than to invalidate more practical previous findings. The claim that climatology is mature, however, was too much for many.

Judith Curry returned:

1. Climate science is NOT a mature field. Stay tuned for more and more surprises . . .

Hard to answer such a argumentative heavy weight comment.

There are also some sensible people there, for example Michael answered Curry:

The second [surprises] is not in any way ruled out by the first [maturity].
There are plenty of new discoveries, even in mature fields.

It it interesting that Curry welcomes surprises and uncertainty so much. The surprises are what worry me the most. I guess that is my conservative side. People have worried about climate change through the ages because the climate is so important to us. Now we are messing with the climate as if we know exactly what we are doing and it's gonna be great.

Fitting to the level of the "debate" in the comments at Climate Etc., dlb wrote:

Perhaps he meant a manure field?
Glad to know I wasn’t the only one whose jaw dropped after reading that.

Most others also just expressed their disbelieve. What comes closest to an argument is: scientists (economists, politicians) have been wrong before, thus climate science is not mature. Okay, maybe I am too generous calling that almost an argument. I hope I did not miss any arguments, the "discussion" was somewhat derailed by people arguing against the greenhouse effect. Also fitting to the level of the comments at Climate Etc. I have no idea how an improvement in scientific understanding should result from that blog.

What makes a scientific field mature?

So what is a "manure" scientific field? Three important aspects are probably: mass, time and networking.

But let me first explain what maturity is not. Calling a field mature does not mean that no discoveries will be made any more, it also does not mean that predictions are perfect and confidence levels are zero. Science is not religion, if you cannot handle uncertainty, you should not be debating science.

Let's contrast a mature with a young science. For example the beginning of discovery of the greenhouse effect. From Wikipedia: "The existence of the greenhouse effect was argued for by Joseph Fourier in 1824. The argument and the evidence was further strengthened by Claude Pouillet in 1827 and 1838, and reasoned from experimental observations by John Tyndall in 1859, and more fully quantified by Svante Arrhenius in 1896."These single scientists could easily have been completely wrong. The years indicate that they did not communicate with each other about the topic. There was thus no competition for who was smarter. At that time it could have happened that someone found that the arguments of Fourier were nice, but the observation do not show the effect. That the measurements of Tyndall were carefully performed, but his instrument does not measure what he thought it did. That there is a greenhouse effect, but contrary to the findings of Arrhenius CO2 is completely irrelevant. And at the time, it would have been possible that negative feedbacks are so strong, that any additional radiative forcing by CO2 does not influence the surface temperature. And a Galileo might have found a stupid calculation error in the works of any of these scientists.

Mass. There are thousands of climate researchers and in I just found 112,598 articles on the Web of Science about "global warming" or "climate change". These article will not all be studying climate change, but a large part will. That alone makes a stupid calculation errors nearly impossible. Having multiple people working on the same topic also provides a sparring partner to discuss and compete with. The weight of the evidence is huge, not comparable to previous times the media or scientists called alarm or called for more research.
Networking. Mass allows some scientists to specialize. For example, some people work on radiative transfer for most of their career. They study the importance of various assumptions, try out various methods of solving a problem, solve various problems with radiative transfer (greenhouse effect, passive and active remote sensing) write textbooks, collaborate with scientists working on radiative transfer in other fields, build joint validation programs for radiative transfer codes, and so on. A specialist is less likely to make mistakes as a newby, the networking weaves a scientific field into the the complete network of scientific theories, methods and tools. (Nothing against newbies, a growing science will have lots of them and they can help with fresh ideas.)

If the physical basis of climate change were found to be wrong, this would likely affect many other sciences via this network. Anything is theoretically possible, but this sure makes it much less likely. The first thing many climate change dissenters learn about is the greenhouse effect itself. Unfortunately many get stuck with this very well connected theory. It is much more likely to find problems with aspects that are specific to climatology, the climatological response of the oceans, vegetation and clouds, for example. What will happen with extreme weather, and all the various impacts? These are difficult questions, but they are not questions that are solved by one Galileo paper. If you want to be that guy, please have a look at such topics, not the greenhouse effect and make the "debate" at least a little more intelligent. Even better, take an objective look at these topics, that increases the chance you will contribute to our understanding of climate change.

Time. Time is important first of all as the amount of time a specialist spends on a topic. Secondly, science takes place in the scientific literature. Performing a study, writing it up and getting it published can easily take a year or longer. While the main ideas might be known from conferences, an idea can only be fully tested after publication. And the response again easily needs a year. Thus science takes time. Also creativity takes time. The longer no new idea turns out to change the main picture much, the less likely it becomes that that happens. And also for creativity mass is important, more people have more funky ideas.

How does a mature scientific field work?

Discoveries. That does not mean that no new discoveries are made. In my own field homogenization we have made spectacular progress the last decade. Modern homogenization methods are now twice as good as traditional ones. Better methods have increased scientific confidence that the temperature trend is robust, but did not change the trend much. (This may be different for daily data, used to study changes in extreme weather, where non-climatic changes are expected to be more important.)

While the homogenization of the annual means is a "manure" science, the homogenization of daily data is in its infancy. Some of my colleagues object to me calling annual homogenization mature, because they still have many ideas to improve it. I hope that after this post, people will understand that I do not see that as a contradiction. Science is never finished, but that does not mean that we know nothing.

Bias. In the beginning, when there is just one person or a few groups working on a problem, they may be tempted to exaggerate the importance of their problem. Most scientists are rather conservative when it comes to making strong claims, but scientists are also humans and some may be tempted by external incentives, although disingenuous pseudo-skeptics like to exaggerate this problem. Calling something a problem helps attracting more people and funding. Exaggeration is relatively easy in this stage as the uncertainties are high and are high due to ignorance.

When a field gets larger, every speciality has an incentive to exaggerate the importance of their speciality. A solar physicist is tempted to claim it is the sun, but hindered by the evidence. I went into science to understand the world a little better and maybe to show off my skills. Were I driven by monetary incentives, as a naive economist might expect, I would be tempted to claim large uncertainties due to non-climatic changes, that would make my field more important. Even if Anthony Watts thinks otherwise, it would be bad for my career to claim that climate data is fine. Making unscientific claims WUWT-style would hurt my career even more and take the fun out of being a scientist.

Because of the incentives of the solar physicist to claim that global warming is due to the sun and my incentive to claim it is non-climatic, the chance of a bias in the big picture is much reduced for a "manure" science. Our understanding will keep on improving and estimates will change, but at this stage I would no longer expect any biases in the basic science: the changes will go in any direction.

Judith Curry likes the word uncertainty. Towards other scientists she can claim that she intended it the way science uses the word: we do not know the exact value, it lies in a confidence range. It could be higher, it could be lower. To her audience that sounds like: they know nothing, they are not sure about climate change, maybe there is no problem after all. Curry's audience hears bias! And is shocked that someone dares to call climatology a mature science.

Thursday, 1 May 2014

How do you explain climate modelling in just 12 minutes? Gavin Schmidt, global climate modeller at NASA GISS, manages to do so. A beautiful talk, I would have been proud to have given it.

As a variability kind of guy, I especially like the introduction. The fundamental problem of climate science is that we have so many orders of magnitude in scale to cover. Spatially the scales go from microscopic dust particle (aerosols) to the size of the Earth; 14 orders of magnitude. In time a similar range is important from fast chemical reaction times to the millennia during which climate is changing. You could easily claim even more orders of magnitude. The chemical composition of the aerosols is, for example, also important for how quickly clouds develop and rain out. The variability over all these scales and how it changes with scale is one of the fascinating aspects of the climate system.

All the scales and all the processes dependent on each other. The aerosols are needed to build clouds. If there are a lot, you get many cloud droplets, which together have a huge surface and the cloud will be very white. If there are only little aerosols, you get less and bigger cloud droplets. To produce rain, less of these large drops need to collide together and rain can builds easier. These clouds, especially the huge shower systems in the tropics, again drive the circulation of the atmosphere, which determines which kinds of vegetation grows where, which again influences ... As Gavin Schmidt states: "You can't understand climate change in pieces. It's the whole, or it's nothing."

You can understand the processes in pieces up to a limit. People measure the chemical properties and shapes of aerosols in the laboratory. Instrumented aircrafts fly through the clouds and measure the sizes of the aerosols and cloud droplets. Simultaneously, remote sensing instruments on the ground and in space measure these clouds, so that the measurements can be compared with each other. The satellites validated at these small scales can then provide the global overview. In modelling the huge range of scales is bridged by a range of models, from large eddy simulation models that resolve the turbulence in the atmosphere, to cloud resolving models that model all the cloud processes in detail, to regional climate models that take land surface into account, to finally global models with ocean and ice models underneath. These detailed models are validated by measurements and themselves used to validate the larger-scale and global climate models.

Models are simplifications and thus by definition "wrong", but they do have skill. Quite an amazing skill if you realise that all the above mentioned and many further processes are only coded at the smallest scale of the model. The highs and lows and their fronts, the hurricanes and shower systems, El Nino and the North Atlantic Oscillation, they all emerge from the interaction of all these processes. This explains the title of the talk says: "The emergent patterns of climate change".

The models have skill, but they are not perfect. Much is simplified. Effective parameters based on measurements unavoidably represent the current climate. Humanity is taking the climate system into uncharted waters and we will only notice which simplifications were too strong when it is too late. To speak with Judith Curry, there is lots of uncertainty. Contrary to Curry, this uncertainty is what worries me most. Uncertainty goes both ways. The word does not mean that it has be turn out for the best. We are messing with a complex climate system upon which our existence depends and we cannot know with certainty what will happen. Not a conservativething to do.

Enjoy the great talk. If you are interested in climate change, it will be worth your 12 minutes.